Meloidogyne enterolobii MeMSP1 effector targets the glutathione-S-transferase phi GSTF family in Arabidopsis to manipulate host metabolism and promote nematode parasitism

Meloidogyne enterolobii is an emerging root-knot nematode species that overcomes most of the nematode resistance genes in crops. Nematode effector proteins secreted in planta are key elements in the molecular dialogue of parasitism. Here, we show the MeMSP1 effector is secreted into giant cells and promotes M. enterolobii parasitism. Using co-immunoprecipitation and bimolecular fluorescent complementation assays, we identified glutathione-S-transferase phi GSTFs as host targets of the MeMSP1 effector. This protein family plays important roles in plant responses to abiotic and biotic stresses. We demonstrate that MeMSP1 interacts with all Arabidopsis GSTF. Moreover, we confirmed that the N-terminal region of AtGSTF9 is critical for its interaction, and atgstf9 mutant lines are more susceptible to root-knot nematode infection. Combined transcriptome and metabolome analyses showed that MeMSP1 affects the metabolic pathways of Arabidopsis thaliana, resulting in the accumulation of amino acids, nucleic acids, and their metabolites, and organic acids and the downregulation of flavonoids. Our study has shed light on a novel effector mechanism that targets plant metabolism, reducing the production of plant defence-related compounds while favouring the accumulation of metabolites beneficial to the nematode, and thereby promoting parasitism.

Online prediction for respiratory movement compensation: a patient-specific gating control for MRI-guided radiotherapy

BACKGROUND

This study aims to validate the effectiveness of linear regression for motion prediction of internal organs or tumors on 2D cine-MR and to present an online gating signal prediction scheme that can improve the accuracy of MR-guided radiotherapy for liver and lung cancer.

MATERIALS AND METHODS

We collected 2D cine-MR sequences of 21 liver cancer patients and 10 lung cancer patients to develop a binary gating signal prediction algorithm that forecasts the crossing-time of tumor motion traces relative to the target threshold. Both 0.4 s and 0.6 s prediction windows were tested using three linear predictors and three recurrent neural networks (RNNs), given the system delay of 0.5 s. Furthermore, an adaptive linear regression model was evaluated using only the first 30 s as the burn-in period, during which the model parameters were adapted during the online prediction process. The accuracy of the predicted traces was measured using amplitude metrics (MAE, RMSE, and R2), and in addition, we proposed three temporal metrics, namely crossing error, gating error, and gating accuracy, which are more relevant to the nature of the gating signals.

RESULTS

In both 0.6 s and 0.4 s prediction cases, linear regression outperformed other methods, demonstrating significantly smaller amplitude errors compared to the RNNs (P < 0.05). The proposed algorithm with adaptive linear regression had the best performance with an average gating accuracy of 98.3% and 98.0%, a gating error of 44 ms and 45 ms, for liver cancer and lung cancer patients, respectively.

CONCLUSION

A functional online gating control scheme was developed with an adaptive linear regression that is both more cost-efficient and accurate than sophisticated RNN based methods in all studied metrics.